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Search Platform · 2025
Semantic Search Engine
Vector-based search engine that understands user intent rather than keyword matching.
- Model
- OpenAI text-embedding-3
- Category
- NLP & Search
- Year
- 2025
- Focus
- Vector DB, Embeddings, Python
Traditional search fails when users do not know the exact terminology. This engine maps 10 million documents into a semantic space.
Users can search using completely different phrasing. A search for "how to fix my slow PC" matches "Windows performance optimization techniques" with high confidence.
The architecture uses a custom hierarchical clustering approach to speed up vector similarity searches, making it viable for real-time auto-complete.
- 10Mdocuments indexed
- < 50msquery latency
- +45%search success rate
